Morphology-Related Foot Function Analysis: Implications for Jumping and Running

A comparison of machine learning models' accuracy in predicting lower-limb joints' kinematics, kinetics, and muscle forces from wearable sensors

A combination of wearable sensors' data and Machine Learning (ML) techniques has been used in many studies to predict specific joint angles and moments. The aim of this study was to compare the performance of four different non-linear regression ML models to estimate lower-limb joints' kinematics, kinetics, and muscle forces using Inertial Measurement Units (IMUs) and electromyographys' (EMGs) data. Seventeen healthy volunteers (9F, 28 ± 5 years) were asked to walk over-ground for a minimum of 16 trials. For each trial, marker trajectories and three force-plates data were recorded to calculate pelvis, hip, knee, and ankle kinematics and kinetics, and muscle forces (the targets), as well as 7 IMUs and 16 EMGs. The features from sensors' data were extracted using the Tsfresh python package and fed into 4 ML models; Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine, and Multivariate Adaptive Regression Spline for targets' prediction. The RF and CNN models outperformed the other ML models by providing lower prediction errors in all intended targets with a lower computational cost. This study suggested that a combination of wearable sensors' data with an RF or a CNN model is a promising tool to overcome the limitations of traditional optical motion capture for 3D gait analysis.

The Acute Influence of Running-Induced Fatigue on the Performance and Biomechanics of a Countermovement Jump

Lower limb kinematics and kinetics during the landing phase of jumping might change because of localized muscle fatigue. This study aimed to investigate the acute influence of running-induced fatigue on the performance and lower limb kinematics and kinetics of a countermovement jump. A running-induced fatigue protocol was applied to fifteen male subjects. Participants were asked to perform three successful countermovement jumps before and after fatigue. Kinematic and kinetic data were collected to compare any fatigue influences. Wilcoxon signed-rank tests and paired-sample t-tests were used to analyze the data. Running-induced fatigue did not significantly change vertical jump height and peak vertical ground reaction forces (GRF) during the push-off and landing phases. Lower limb biomechanics significantly changed, especially kinematic parameters. During the push-off phase, fatigue resulted in an increased ankle peak inversion angle, knee minimal flexion angle, knee peak abduction angle, and hip peak flexion moment. In addition, the range of motion (ROM) of the ankle and knee joints in the frontal plane was also increased. Certain parameters decreased as a result of fatigue, such as the ankle peak internal rotation angle, hip peak abduction angle, the ROM of the ankle joint in the sagittal plane, and ROM of the hip joint in the frontal plane. During the landing phase, the peak inversion angle and peak external rotation angle of the ankle joint, peak abduction angle of the knee and hip joint, ROM of the ankle joint in the horizontal plane, ROM of the ankle and knee joint in the frontal plane were all increased as a result of fatigue. The knee peak flexion moment and hip peak extension moment, however, were decreased. Under fatigue conditions, lower limb kinetics and kinematics were changed during both the push-off and landing phases. More attention should be focused on the landing phase and the last period of the push-off phase due to potentially higher risks of injury. The findings of the current study may be beneficial to athletes and coaches in preventing jumping related injuries.

Editorial “Biomechanical Spectrum of Human Sport Performance”

Several parameters can influence our health capital today and can have a negative impact on our performance, whether physiological or mechanical. Indeed, our health and wellbeing are influenced by a range of social, cultural, economic, psychological, and environmental factors across our lives. These change as we progress through the key transition points in life—from infancy and childhood through our teenage years to adulthood, working life, retirement and the end of life. Sport can be a vector that links many of these factors. Whether it is high-performance sport or sedentary practice, sport is very important for the improvement of psychological wellbeing and physical health. Our overarching aim was to increase quality of life. Sedentary practice can increase mobility and reduce the risk of disease, so changing adults’ behavior through sedentary practice could reduce illness and decrease costs to society concerning health problems. Furthermore, a higher frequency of practice can lead to improvements in technique and optimized performance. Our objective is to summarize the latest research in sport science and to quantify the most important parameters influencing human performance related to the health sciences for all age groups, throughout their lives.